K-NRM. The kernel-based neural ranking model employs kernels to produce soft-match signals between words [1]}. Given a query and document, it constructs a translation matrix from word pair similarities. These similarities are based on word embeddings that are learned jointly with the ranking model. In the next layer, kernels generate \(K\)  soft-TF ranking features by counting soft matches between pairs of words from queries and documents at multiple levels. The model combines these soft-TF signals and feeds them into a learning to rank layer to produce the final score.
